Sectors Performance
Sector Price Performance Distribution
For Date: 2026-09-04
Performance Heatmap (%)
| Sector | 1 Day | 1 Week | 1 Month | 3 Months | 6 Months | YTD | 1 Year |
| Technology | 0.70 | 0.42 | 0.20 | -2.93 | 34.25 | 30.10 | 43.37 |
| Industrials | 0.41 | 0.08 | -5.97 | -0.26 | 0.13 | 11.52 | 17.18 |
| Utilities | 0.12 | 2.01 | -2.34 | -1.33 | -7.65 | 1.10 | 5.74 |
| Materials | -0.34 | -0.47 | 0.85 | 1.96 | 1.83 | 14.62 | 16.92 |
| Real Estate | -0.72 | -0.41 | -2.75 | -0.18 | 1.95 | 10.49 | 8.85 |
| Financials | -0.79 | 0.68 | 0.38 | 11.71 | 13.79 | 6.68 | 9.15 |
| Consumer Staples | -0.80 | -0.47 | -0.93 | 3.82 | -1.74 | 10.23 | 7.79 |
| Energy | -0.87 | 0.16 | 9.47 | 9.82 | 15.57 | 42.26 | 48.29 |
| Health Care | -1.04 | 0.53 | 5.77 | 13.24 | 10.10 | 11.19 | 26.92 |
| Communication Services | -1.19 | 0.51 | -0.01 | -0.70 | -5.15 | -3.61 | -0.57 |
| Consumer Discretionary | -1.33 | -1.44 | -2.86 | -1.81 | -0.88 | -2.52 | -1.51 |
Ask the market a question. Get a calculated answer.
The AI is not a chatbot bolted onto a document store. It calls the same analytics engine that powers every screen on this platform — so what comes back is a number it computed from raw history, with the command that produced it.
86,000+ instruments
Global equities, ETFs, funds, options, FX, commodities, crypto, economics, filings, transcripts and news — one normalised symbol universe with adjusted history.
A real analytics engine
Screening, backtesting, technicals, options analytics, correlations, seasonality and factor models — computed on demand from raw prices, never a stale cache.
It shows its working
Answers arrive with the charts, tables and tool calls behind them, so you can check the number instead of trusting a paraphrase.
Your own documents
Upload filings, decks and research. Ask across them and the answer cites the page it came from.
Agents and workflows
Multi-step research that runs the platform's tools for you — screen, pull the history, compute, compare, then write it up.
MCP, CLI and API
The same command catalogue from Claude, your own agent, a shell or your pipeline. The answer on screen is the answer your job gets at 4am.
You ask
“How does NVDA usually trade through earnings?”
It calls
→ ka.options_expected_move(NVDA)
It answers
NVDA has averaged a 9.2% absolute move on the day after earnings and closed higher 67% of the time. Two in three reactions land between −4.2% and +16.3% — the distribution is skewed right, not symmetric.
Every figure computed live from our own history — not scraped, not summarised.
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